Intelligent safety rope system

Abstract

The invention discloses an intelligent safety rope system, and belongs to the technical field of safety, and the system comprises an intelligent safety rope body which comprises a left hook and a right hook, each hook is provided with a sensor, and the sensors are used for detecting whether the hooks are reliably hung on a fixed object or not; the main control chip is used for controlling the operation of the whole system, processing sensor data, and performing judgment and decision making according to preset parameters; and the communication module comprises a northbound data interface unit and a southbound data interface unit. According to the method, the working efficiency is effectively improved, and efficient completion of each task is ensured; a black box data storage function is arranged in the system, so that powerful support is provided for accident tracing after an accident occurs; through detailed analysis of operation data, accident causes can be quickly positioned, and responsibility determination is facilitated, so that a scientific basis is provided for improving safety measures; due to the existence of the function, safety management is more refined and intelligent, and safety production of enterprises is protected.

Description

Technical Field

[0001] The invention belongs to the field of safety technology, and in particular relates to an intelligent safety rope system. Background Art

[0002] With the development of industry, modern power plants have a large area, numerous personnel and equipment, and complex internal structures. Operations at heights and in confined spaces are extremely dangerous. The positioning of power personnel plays a very important role in improving the safety protection function and supervision effect of power operations. The personnel positioning system can realize the informatization, dataization, intelligence, and refinement of personnel management, and comprehensively improve safety management capabilities.

[0003] The safety rope consists of the following parts: Safety rope: usually made of high-strength, wear-resistant and tensile-resistant materials, such as wire rope or synthetic fiber rope, used to bear the weight of the operator; Connector: including hooks, buckles, etc., used to connect the safety rope to the safety belt or other safety equipment. Safety belt: equipment worn by the operator, usually including belts, shoulder straps and leg straps, connected to the safety rope. Buffer device: such as fall arrester, shock absorber, etc., used to reduce the impact force and protect the operator when a fall occurs. Fixed point: Anchor point used to fix the safety rope, which can be an anchor fixed to the building or other structure.

[0004] The existing high-altitude work safety rope system generally has the following problems:

[0005] Single hook control method: Traditional safety rope systems can usually only control the left hook through the left side and the right hook through the right side. The interlocking function between the hooks cannot be achieved, which poses a safety hazard.

[0006] Insufficient safety performance: The existing safety rope system mainly relies on mechanical structure to achieve safety protection, lacks intelligent safety measures, cannot monitor the physiological state of workers and environmental changes in real time, and is difficult to effectively prevent the occurrence of safety accidents.

[0007] Difficulty in tracing accidents: The existing safety rope system lacks data recording function and cannot record relevant data of the operation process, making it difficult to trace the cause of the accident, which is not conducive to determining responsibility and improving safety measures.

[0008] Based on this, the present invention designs an intelligent safety rope system to solve the above problems. Summary of the invention

[0009] The purpose of the present invention is to propose an intelligent safety rope system in order to solve the problems of the existing safety rope system having a single hook control mode, insufficient safety performance, and difficulty in accident tracing.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] Intelligent safety rope system, including:

[0012] Smart safety rope body: includes two hooks on the left and right, each hook is equipped with a sensor to detect whether the hook is reliably hung on a fixed object;

[0013] Main control chip: used to control the operation of the entire system, process sensor data, and make judgments and decisions based on preset parameters;

[0014] Communication module: including northbound data interface unit and southbound data interface unit;

[0015] Eastbound data interface unit: includes electromagnetic locking mechanism and safety / warning voice broadcast function;

[0016] Westward data interface unit: including environmental temperature and humidity sensors, height sensors, weight sensors, tilt sensors, camera input modules and falling / weightlessness detection modules;

[0017] Black box data storage unit: used to record all relevant data of the operation process, so as to facilitate the subsequent tracing of the cause of the accident and the improvement of safety measures.

[0018] As a further description of the above technical solution:

[0019] When one hook in the intelligent safety rope body is not properly hung, the system will automatically lock the other hook to prevent the safety rope from falling off.

[0020] As a further description of the above technical solution:

[0021] The northbound data interface unit is used to send the safety rope status data to the background server and push it to the monitoring screen and mobile phone;

[0022] The southbound data interface unit is used to realize the wireless intercom function between aerial workers and ground personnel through the ground handling system.

[0023] As a further description of the above technical solution:

[0024] In the eastward data interface unit, when a safety risk is detected, the system will automatically lock the hook and issue a warning voice to remind the operator to pay attention to safety.

[0025] As a further description of the above technical solution:

[0026] The sensor in the west-bound data interface unit is used to monitor the physiological state and environmental changes of the operator in real time, and transmit the data to the main control chip for analysis and processing. The inclination includes the detection of human body shaking, swaying and falling.

[0027] As a further description of the above technical solution:

[0028] The data stored in the black box includes the status of the safety rope, environmental parameters, and operator behavior.

[0029] As a further description of the above technical solution:

[0030] The sensors in the westbound data interface unit also include a heart rate sensor, a body temperature sensor and a breathing sensor.

[0031] As a further description of the above technical solution:

[0032] The main control chip is specifically a microcontroller, and the data analysis process includes monitoring and early warning of the operator's physiological data, environmental risk assessment, and assessment of personnel behavior risks;

[0033] Environmental risk assessment specifically includes the following steps:

[0034] Data collection, including ambient temperature, humidity, safety rope tension, and safety rope use frequency;

[0035] Data preprocessing to remove missing values, outliers and duplicate data in the acquired data;

[0036] Model training, select the linear regression model to predict environmental data:

[0037] The formula of the linear regression model is: y = Xθ + ε

[0038] Where y is the predicted value, X is the feature matrix, θ is the parameter vector, and ε is the error term;

[0039] The obtained data set is divided into a training set and a test set;

[0040] Train the model using the training set;

[0041] Optimize model parameters by minimizing the loss function;

[0042]

[0043] Among them, y i is the actual value, is the predicted value;

[0044] Model evaluation,The performance of the model was evaluated using mean squared error, root mean square error, and mean absolute error.

[0045] The behavioral risk assessment includes the following steps:

[0046] Whether the rope fixing point is safe. If the rope fixing point is not firm, the risk value is high. A binary variable is used to describe it, where 1 means unsafe and 0 means safe:

[0047] P1 ={0,1};

[0048] The tightness of the rope during movement. Ropes that are too loose or too tight will increase the risk:

[0049]

[0050] Where L actual is the actual rope length, L ideal is the ideal rope length, P 2 The closer the value is to 0, the safer it is;

[0051] Working posture stability, unstable posture may increase the risk of imbalance or falling:

[0052]

[0053] The comprehensive behavioral risk formula is: b =ω 1 ·P 1 +ω 2 ·P 2 +ω 3 ·P 3 .

[0054] Postural stability, including posture deviation calculation:

[0055] Front and rear tilt angle deviation P pitch :

[0056] P pitch =|θ pitch -θ pitch-ideal |

[0057] Among them, θ pitch is the real-time fore-aft tilt angle, θ pitch-ideal is the ideal front and rear tilt angle;

[0058] Left and right tilt angle deviation P roll :

[0059] P roll =|θ roll -θ roll-ideal |

[0060] Among them, θ roll is the real-time left and right tilt angle, θ roll-ideal is the ideal left and right tilt angle;

[0061] Horizontal rotation angle deviation P yaw :

[0062] P yaw =|θ yaw -θ yaw-ideal |

[0063] Among them, θ yaw is the horizontal rotation angle, θ yaw-ideal is the ideal rotation angle;

[0064] The center of gravity offset is obtained by calculating the coordinates of key points. The key points of the body (such as shoulders, waist, and hips) are measured by the camera, and the position deviation of the current center of gravity relative to the ideal center of gravity is calculated:

[0065]

[0066] Among them, (x cg ,y cg , z cg ) is the current center of gravity coordinate, (x cg-ideal ,y cg-ideal , z cg-ideal ) are the coordinates of the ideal center of gravity;

[0067] Comprehensive posture deviation calculation integrates each deviation parameter to obtain a comprehensive posture deviation score, and combines different posture parameter deviations by weighted average:

[0068] P 3 =ω pitch ·P pitch +ω roll ·P roll +ω yaw ·P yaw +ω cg ·P cg

[0069] Among them, ω pitch ,ω roll ,ω yaw ,ω cg is the weight of each posture deviation.

[0070] As a further description of the above technical solution:

[0071] The data stored in the black box includes compression processing of the data;

[0072] The data compression process is as follows: the observation vector is transformed into y = Φz + n, the variance of the noise is taken as a constant, the observation data is taken as a Gaussian random vector, and the signal model adopts a Bayesian model with feedback. The signal obeys the Laplace prior distribution, where φ represents the Gaussian random measurement matrix. Using the simplest prior distribution, the vector z is assumed to obey an independent distribution with a Gaussian distribution with a mean of 0. The hyperparameter of the prior distribution itself is α = [α 1 , α 2 , …α N ] TObey the gamma distribution with hyperparameters a and b, simplify the model, let a=1, 2b=λ, and according to the identity in probability theory, get the posterior probability of z:

[0073] p(z|y,λ,σ 2 )=p(y|z,σ 2 ) / p(y|λ,σ 2 )

[0074] Therefore, the posterior probability distribution follows a Gaussian distribution N(u,∑) with a mean of u and a variance of ∑. Using the augmentation idea, it is converted into the posterior of complete data.

[0075] According to the Bayesian formula,

[0076] p(z,λ,β,α|y)=p(z|λ,β,α,y)p(λ,β,α,|y)

[0077]

[0078] in express Derivative of τ;

[0079] Removing the terms that are not related to α yields:

[0080]

[0081] Using the maximum likelihood estimation idea to estimate α i Updates available when hour,

[0082]

[0083] Otherwise α i =0, for the convenience of calculating p i and q i , update p i and q i , can be obtained

[0084] Note when using the safety rope system:

[0085] Inspection: Before use, all parts such as safety ropes, safety belts, connectors, etc. must be checked to see if they are intact.

[0086] Wearing: Operators must wear safety belts correctly and ensure that all connecting parts are properly connected and locked.

[0087] Fixing: Fix the other end of the safety rope to a reliable fixing point and ensure it is firmly fixed.

[0088] Operation: During the operation, you should always stay connected to the safety rope system and be careful not to exceed the limited working range of the safety rope.

[0089] Maintenance: After use, the safety rope system should be cleaned and maintained to keep it in good working condition.

[0090] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0091] In the present invention, the safety is better: through the left and right interlocking hooks and intelligent safety measures, the probability of safety accidents is effectively reduced, and the personal safety of the operators is guaranteed; the work efficiency is improved: the intelligent safety rope system is equipped with a wireless intercom function, so that the operators can achieve instant communication during work, which greatly facilitates on-site coordination and command; at the same time, the black box data storage function can record the operation process in real time, effectively improve work efficiency, and ensure the efficient completion of various tasks; convenient accident tracing: the system's built-in black box data storage function provides strong support for accident tracing after the accident; through detailed analysis of the operation data, the cause of the accident can be quickly located, which is helpful for responsibility identification, thereby providing a scientific basis for improving safety measures; the existence of this function makes safety management more refined and intelligent, and escorts the safe production of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 This is a schematic diagram of the principle connection of the intelligent safety rope system proposed in the present invention. DETAILED DESCRIPTION

[0093] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0094] Please refer to the attached figure, Example 1:

[0095] Intelligent safety rope system, including:

[0096] Smart safety rope body: includes two hooks on the left and right, each hook is equipped with a sensor to detect whether the hook is reliably hung on a fixed object;

[0097] Main control chip: used to control the operation of the entire system, process sensor data, and make judgments and decisions based on preset parameters;

[0098] Communication module: including northbound data interface unit and southbound data interface unit;

[0099] Eastbound data interface unit: includes electromagnetic locking mechanism and safety / warning voice broadcast function;

[0100] Westward data interface unit: including environmental temperature and humidity sensors, height sensors, weight sensors, tilt sensors, camera input modules and falling / weightlessness detection modules;

[0101] Black box data storage unit: used to record all relevant data of the operation process, so as to facilitate the subsequent tracing of the cause of the accident and the improvement of safety measures.

[0102] When one hook in the intelligent safety rope body is not hung properly, the system will automatically lock the other hook to prevent the safety rope from falling off.

[0103] The northbound data interface unit is used to send the safety rope status data to the background server and push it to the monitoring screen and mobile phone;

[0104] The southbound data interface unit is used to realize the wireless intercom function between aerial workers and ground personnel through the ground handling system.

[0105] In the eastward data interface unit, when a safety risk is detected, the system will automatically lock the hook and issue a warning voice to remind the operator to pay attention to safety.

[0106] The sensors in the westbound data interface unit are used to monitor the physiological state and environmental changes of the operators in real time, and transmit the data to the main control chip for analysis and processing. The inclination includes the detection of human body shaking, swaying and falling.

[0107] The data stored in the black box includes the status of the safety rope, environmental parameters, and operator behavior.

[0108] Sensors in the westbound data interface unit also include a heart rate sensor, a body temperature sensor, and a respiration sensor.

[0109] The main control chip is specifically a microcontroller. The data analysis process includes monitoring and early warning of the operator's physiological data, environmental risk assessment, and assessment of personnel behavior risks:

[0110] Monitoring and early warning are carried out using a neural network prediction algorithm.

[0111] Embodiment 2:

[0112] Intelligent safety rope system, including:

[0113] Smart safety rope body: includes two hooks on the left and right, each hook is equipped with a sensor to detect whether the hook is reliably hung on a fixed object;

[0114] Main control chip: used to control the operation of the entire system, process sensor data, and make judgments and decisions based on preset parameters;

[0115] Communication module: including northbound data interface unit and southbound data interface unit;

[0116] Eastbound data interface unit: includes electromagnetic locking mechanism and safety / warning voice broadcast function;

[0117] Westward data interface unit: including environmental temperature and humidity sensors, height sensors, weight sensors, tilt sensors, camera input modules and falling / weightlessness detection modules;

[0118] Black box data storage unit: used to record all relevant data of the operation process, so as to facilitate the subsequent tracing of the cause of the accident and the improvement of safety measures.

[0119] When one hook in the intelligent safety rope body is not hung properly, the system will automatically lock the other hook to prevent the safety rope from falling off.

[0120] The northbound data interface unit is used to send the safety rope status data to the background server and push it to the monitoring screen and mobile phone;

[0121] The southbound data interface unit is used to realize the wireless intercom function between aerial workers and ground personnel through the ground handling system.

[0122] In the eastward data interface unit, when a safety risk is detected, the system will automatically lock the hook and issue a warning voice to remind the operator to pay attention to safety.

[0123] The sensors in the westbound data interface unit are used to monitor the physiological state and environmental changes of the operators in real time, and transmit the data to the main control chip for analysis and processing. The inclination includes the detection of human body shaking, swaying and falling.

[0124] The data stored in the black box includes the status of the safety rope, environmental parameters, and operator behavior.

[0125] Sensors in the westbound data interface unit also include a heart rate sensor, a body temperature sensor, and a respiration sensor.

[0126] The main control chip is specifically a microcontroller, and the data analysis process includes monitoring and early warning of the operator's physiological data, environmental risk assessment, and assessment of personnel behavior risks;

[0127] The behavioral risk assessment includes the following steps:

[0128] Whether the rope fixing point is safe. If the rope fixing point is not firm, the risk value is high. A binary variable is used to describe it, where 1 means unsafe and 0 means safe:

[0129] P 1 ={0,1};

[0130] The tightness of the rope during movement. Ropes that are too loose or too tight will increase the risk:

[0131]

[0132] Where L actual is the actual rope length, L ideal is the ideal rope length, P 2 The closer the value is to 0, the safer it is;

[0133] Working posture stability, unstable posture may increase the risk of imbalance or falling:

[0134]

[0135] The comprehensive behavioral risk formula is: b =ω 1 ·P 1 +ω 2 ·P 2 +ω 3 ·P 3 .

[0136] Postural stability, including posture deviation calculation:

[0137] Front and rear tilt angle deviation P pitch :

[0138] P pitch =|θ pitch -θ pitch-ideal |

[0139] Among them, θ pitch is the real-time fore-aft tilt angle, θ pitch-ideal is the ideal front and rear tilt angle;

[0140] Left and right tilt angle deviation P roll :

[0141] P roll =|θ roll -θ roll-ideal |

[0142] Among them, θ roll is the real-time left and right tilt angle, θ roll-ideal is the ideal left and right tilt angle;

[0143] Horizontal rotation angle deviation P yaw :

[0144] P yaw =|θ yaw -θ yaw-ideal |

[0145] Among them, θ yaw is the horizontal rotation angle, θ yaw-ideal is the ideal rotation angle;

[0146] The center of gravity offset is obtained by calculating the coordinates of key points. The key points of the body (such as shoulders, waist, and hips) are measured by the camera, and the position deviation of the current center of gravity relative to the ideal center of gravity is calculated:

[0147]

[0148] Among them, (x cg ,y cg , z cg ) is the current center of gravity coordinate, (x cg-ideal ,y cg-ideal , z cg-ideal ) are the coordinates of the ideal center of gravity;

[0149] Comprehensive posture deviation calculation integrates each deviation parameter to obtain a comprehensive posture deviation score, and combines different posture parameter deviations by weighted average:

[0150] P 3 =ω pitch ·P pitch +ω roll ·P roll +ω yaw ·P yaw +ω cg ·P cg

[0151] Among them, ω pitch ,ω roll ,ω yaw ,ω cg is the weight of each posture deviation.

[0152] The data stored in the black box includes the compression processing of the data;

[0153] The data compression process is as follows: the observation vector is transformed into y = Φz + n, the variance of the noise is taken as a constant, the observation data is taken as a Gaussian random vector, and the signal model adopts a Bayesian model with feedback. The signal obeys the Laplace prior distribution, where φ represents the Gaussian random measurement matrix. Using the simplest prior distribution, the vector z is assumed to obey an independent distribution with a Gaussian distribution with a mean of 0. The hyperparameter of the prior distribution itself is α = [α 1 , α 2 , …α N ] T Obey the gamma distribution with hyperparameters a and b, simplify the model, let a=1, 2b=λ, and according to the identity in probability theory, get the posterior probability of z:

[0154] p(z|y,λ,σ 2 )=p(y|z,σ 2 ) / p(y|λ,σ 2 )

[0155] Therefore, the posterior probability distribution follows a Gaussian distribution N(u,∑) with a mean of u and a variance of ∑. Using the augmentation idea, it is converted into the posterior of complete data.

[0156] According to the Bayesian formula,

[0157] p(z,λ,β,α|y)=p(z|λ,β,α,y)p(λ,β,α,|y)

[0158]

[0159] in express Derivative of τ.

[0160] Removing the terms that are not related to α yields:

[0161]

[0162]

[0163] Using the maximum likelihood estimation idea to estimate α i Updates available when hour,

[0164]

[0165] Otherwise α i =0, for the convenience of calculating p i and q i , update p i and q i , can be obtained

[0166] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Intelligent safety rope system, characterized in that: include: Smart safety rope body: includes two hooks on the left and right, each hook is equipped with a sensor to detect whether the hook is reliably hung on a fixed object; Main control chip: used to control the operation of the entire system, process sensor data, and make judgments and decisions based on preset parameters; Communication module: including northbound data interface unit and southbound data interface unit; Eastbound data interface unit: includes electromagnetic locking mechanism and safety / warning voice broadcast function; Westward data interface unit: including environmental temperature and humidity sensor, height sensor, weight sensor, tilt sensor, camera input module and falling / weightlessness detection module; Black box data storage unit: used to record all relevant data of the operation process, so as to facilitate the subsequent tracing of the cause of the accident and the improvement of safety measures.

2. The intelligent safety rope system according to claim 1, characterized in that: When one hook in the intelligent safety rope body is not properly hung, the system will automatically lock the other hook to prevent the safety rope from falling off.

3. The intelligent safety rope system according to claim 1, characterized in that: The northbound data interface unit is used to send the safety rope status data to the background server and push it to the monitoring screen and mobile phone; The southbound data interface unit is used to realize the wireless intercom function between aerial workers and ground personnel through the ground handling system.

4. The intelligent safety rope system according to claim 1, characterized in that: In the eastward data interface unit, when a safety risk is detected, the system will automatically lock the hook and issue a warning voice to remind the operator to pay attention to safety.

5. The intelligent safety rope system according to claim 1, characterized in that: The sensor in the west-bound data interface unit is used to monitor the physiological state and environmental changes of the operator in real time, and transmit the data to the main control chip for analysis and processing. The inclination includes the detection of human body shaking, swaying and falling.

6. The intelligent safety rope system according to claim 1, characterized in that: The data stored in the black box includes the status of the safety rope, environmental parameters, and operator behavior.

7. The intelligent safety rope system according to claim 1, characterized in that: The sensors in the westbound data interface unit also include a heart rate sensor, a body temperature sensor and a breathing sensor.

8. The intelligent safety rope system according to claim 1, characterized in that: The main control chip is specifically a microcontroller, and the data analysis process includes monitoring and early warning of the operator's physiological data, environmental risk assessment, and assessment of personnel behavior risks; Environmental risk assessment specifically includes the following steps: Data collection, including ambient temperature, humidity, safety rope tension, and safety rope use frequency; Data preprocessing to remove missing values, outliers and duplicate data in the acquired data; Model training, select the linear regression model to predict environmental data: The formula of the linear regression model is: y = Xθ + ε Where y is the predicted value, X is the feature matrix, θ is the parameter vector, and ε is the error term; The obtained data set is divided into a training set and a test set; Train the model using the training set; Optimize model parameters by minimizing the loss function; Among them, y i is the actual value, is the predicted value; Model evaluation,The performance of the model was evaluated using mean squared error, root mean square error, and mean absolute error.

9. The intelligent safety rope system according to claim 8, characterized in that: The behavior risk assessment specifically includes the following steps: Whether the rope fixing point is safe. If the rope fixing point is not firm, the risk value is high. A binary variable is used to describe it, where 1 means unsafe and 0 means safe: P1＝{0,1}； The tightness of the rope during movement. Ropes that are too loose or too tight will increase the risk: Where L actual is the actual rope length, L ideal is the ideal rope length. The closer the P2 value is to 0, the safer it is. Working posture stability, unstable posture may increase the risk of imbalance or falling: The comprehensive behavioral risk formula is: b =ω1·P1+ω2·P2+ω3·P3; Postural stability, including posture deviation calculation: Front and rear tilt angle deviation P pitch : P pitch =θ pitch -θ pitch-ideal | Among them, θ pitch is the real-time fore-aft tilt angle, θ pitch-ideal is the ideal front and rear tilt angle; Left and right tilt angle deviation P roll : P roll =θ roll -θ roll-ideal | Among them, θ roll is the real-time left and right tilt angle, θ roll-ideal is the ideal left and right tilt angle; Horizontal rotation angle deviation P yaw : P yaw =|θ yaw -θ yaw-ideal | Among them, θ yaw is the horizontal rotation angle, θ yaw-ideal is the ideal rotation angle; The center of gravity offset is obtained by calculating the coordinates of key points. The key points of the body (such as shoulders, waist, and hips) are measured by the camera, and the position deviation of the current center of gravity relative to the ideal center of gravity is calculated: Among them, (x cg ,y cg , z cg ) is the current center of gravity coordinate, (x cg-ideal ,y cg-ideal , z cg-ideal ) are the coordinates of the ideal center of gravity; Comprehensive posture deviation calculation integrates each deviation parameter to obtain a comprehensive posture deviation score, and combines different posture parameter deviations by weighted average: P3=ω pitch ·P pitch +oh roll ·P roll +oh yaw ·P yaw +oh cg ·P cg Among them, ω pitch ,ω roll ,ω yaw ,ω cg is the weight of each posture deviation.

10. The intelligent safety rope system according to claim 6, characterized in that: The data stored in the black box includes compression processing of the data; The data compression process is as follows: the observation vector is transformed into y = Φz + n, the variance of the noise is taken as a constant, the observation data is taken as a Gaussian random vector, the signal model adopts a Bayesian model with feedback, the signal obeys the Laplace prior distribution, where φ represents the Gaussian random measurement matrix, and the simplest prior distribution is used to assume that the vector z obeys an independent distribution with a mean of 0. The hyperparameter of the prior distribution itself is α = [α1, α2, …α N ] T Obey the gamma distribution with hyperparameters a and b, simplify the model, let a=1, 2b=λ, and according to the identity in probability theory, get the posterior probability of z: p(z|y,λ,σ 2 )=p(y|z,σ 2 ) / p(y|λ,σ 2 ) Therefore, the posterior probability distribution follows a Gaussian distribution N(u,∑) with a mean of u and a variance of ∑. Using the augmentation idea, it is converted into the posterior of complete data. According to the Bayesian formula, p(z,λ,β,α|y)=p(z|λ,β,α,y)p(λ,β,α,|y) in express Derivative of τ; Removing the terms that are irrelevant to α yields: Using the maximum likelihood estimation idea to estimate α i Updates available when hour, Otherwise α i =0, for the convenience of calculating p i and q i , update p i and q i , can be obtained

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